Paquetes
Carga de Datos
especies_primates <-
st_read("https://raw.githubusercontent.com/gf0604-procesamientodatosgeograficos/2021i-datos/main/gbif/primates-cr-registros.csv",
options = c (
"X_POSSIBLE_NAMES=decimalLongitude",
"Y_POSSIBLE_NAMES=decimalLatitude",
quiet = TRUE
)
)
## options: X_POSSIBLE_NAMES=decimalLongitude Y_POSSIBLE_NAMES=decimalLatitude TRUE
## Reading layer `primates-cr-registros' from data source `https://raw.githubusercontent.com/gf0604-procesamientodatosgeograficos/2021i-datos/main/gbif/primates-cr-registros.csv' using driver `CSV'
## Simple feature collection with 4509 features and 50 fields
## Geometry type: POINT
## Dimension: XY
## Bounding box: xmin: -85.96248 ymin: 8.040197 xmax: -82.55949 ymax: 11.15408
## CRS: NA
st_crs(especies_primates) = 4326
cantones <-
st_read(
"https://raw.githubusercontent.com/gf0604-procesamientodatosgeograficos/2021i-datos/main/ign/delimitacion-territorial-administrativa/cr_cantones_simp_wgs84.geojson",
quiet = TRUE
)
provincias <-
st_read(
"https://raw.githubusercontent.com/gf0604-procesamientodatosgeograficos/2021i-datos/main/ign/delimitacion-territorial-administrativa/cr_provincias_simp_wgs84.geojson",
quiet = TRUE
)
cantones <-
especies_primates %>%
st_join(cantones["canton"])
Tabla para cada registro
especies_primates %>%
st_drop_geometry() %>%
dplyr::select(family, species, stateProvince, locality, eventDate) %>%
datatable(
colnames = c("Provincia", "Especie", "Provincia", "Cantones", "Fecha"),
options = list(
searchHighlight = TRUE,
language = list(url = "//cdn.datatables.net/plug-ins/1.10.11/i18n/Spanish.json")
)
)
Especies
Gráfico Pastel
especies_primates %>%
plot_ly(
labels = ~c("Mono aullador","Mono araña","Mono ardillla","Mono carablanca"
),
values = ~c(1994, 599, 453, 1463),
type = "pie")%>%
config(locale = "es")%>%
layout(
title = "",
xaxis = list(showgrid = FALSE,zeroline = FALSE,showticklabels = FALSE
),
yaxis = list( showgrid = FALSE,zeroline = FALSE, showticklabels = FALSE
)
)
Datos Altitud
alt <- getData(
"worldclim",
var = "alt",
res = .5,
lon = -84,
lat = 10
)
altitud <-alt %>% crop(provincias) %>% mask(provincias)
col <- colorNumeric(c("pink", "blue", "red"),
values(altitud),
na.color = "transparent")
Especies
Ateles <- paste0((araña$species),
(araña$provincia),
(araña$locality),
(araña$eventDate)
)
Saimiri <- paste0((ardilla$species),
(ardilla$provincia),
(ardilla$locality),
(ardilla$eventDate)
)
Cebus <- paste0((carablanca$species),
(carablanca$provincia),
(carablanca$locality),
(carablanca$eventDate)
)
Alouatta <- paste0((aullador$species),
(aullador$provincia),
(aullador$locality),
(aullador$eventDate)
)
Mapa
especies_primates %>%
leaflet() %>%
addProviderTiles(providers$OpenStreetMap.Mapnik, group = "OpenStreetMap") %>%
addProviderTiles(providers$Stamen.TonerLite, group = "Stamen Toner Lite") %>%
addProviderTiles(providers$Esri.WorldImagery, group = "Imágenes de ESRI") %>%
addRasterImage(
altitud,
colors = col,
opacity = 0.8,
group = "Altitud") %>%
addCircleMarkers(
data = aullador,
stroke = F,
radius = 4,
fillColor = "#CC6633",
fillOpacity = 1,
popup = Alouatta ,
group = "Alouatta palliata"
) %>%
addCircleMarkers(
data = ardilla,
stroke = F,
radius = 4,
fillColor = "red",
fillOpacity = 1,
popup = Saimiri,
group = "Saimiri oerstedii"
) %>%
addCircleMarkers(
data = araña,
stroke = F,
radius = 4,
fillColor = "#9933FF",
fillOpacity = 1,
popup = Ateles,
group = "Ateles geoffroyi"
) %>%
addCircleMarkers(
data = carablanca,
stroke = F,
radius = 4,
fillColor = "#FFFF00",
fillOpacity = 1,
popup = Cebus,
group = "Cebus capucinus"
) %>%
addLayersControl(
baseGroups = c("OpenStreetMap", "Stamen Toner Lite",
"Imágenes de ESRI"),
overlayGroups = c("Alouatta palliata", "Cebus capucinus",
"Ateles geoffroyi", "Saimiri oerstedii"
,"Altitud")
) %>%
addMiniMap(tiles = providers$Stamen.OpenStreetMap.Mapnik,
position = "bottomleft",
toggleDisplay = TRUE
)